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Model Fit after Pairwise Maximum Likelihood.

M T Barendse1, R Ligtvoet2, M E Timmerman3

  • 1Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University Ghent, Belgium.

Frontiers in Psychology
|May 6, 2016
PubMed
Summary
This summary is machine-generated.

Pairwise maximum likelihood (PML) analysis for discrete data in structural equation modeling requires new fit criteria. Simulation shows PML performs comparably to robust weighted least squares for large sample sizes.

Keywords:
discrete datafit statisticspairwise maximum likelihood analysisweighted least squares analysis

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Area of Science:

  • Statistics
  • Psychometrics
  • Structural Equation Modeling

Background:

  • Maximum likelihood factor analysis for discrete data assumes underlying normally distributed continuous scores.
  • Multivariate response pattern analysis is computationally intensive, leading to the use of pairwise maximum likelihood (PML).
  • Assessing model fit for PML with two-way contingency tables is not well-established.

Purpose of the Study:

  • To propose and evaluate new model fit criteria for pairwise maximum likelihood (PML) analysis of discrete data.
  • To assess the performance of PML in model selection compared to other methods.
  • To investigate the behavior of PML with large sample sizes in structural equation modeling.

Main Methods:

  • Development of novel statistical criteria for evaluating model fit in PML.
  • Conducting a simulation study to test the proposed fit criteria.
  • Comparison of PML performance against robust weighted least squares analysis of polychoric correlations.

Main Results:

  • The proposed fit criteria were evaluated through a simulation study.
  • Pairwise maximum likelihood (PML) demonstrated comparable performance to robust weighted least squares analysis.
  • Effective performance of PML was observed with large sample sizes (N >= 500).

Conclusions:

  • New fit criteria enhance the assessment of model fit in pairwise maximum likelihood (PML) analysis.
  • PML is a viable and effective method for analyzing discrete data in structural equation modeling, especially with large datasets.
  • The findings support the use of PML as an alternative to traditional methods when dealing with computationally intensive analyses of discrete response data.